Power Grid Data Recovery Method Driven by Temporal Composite Diffusion Networks

ObjectiveSmart grid construction drives modern power systems, and distribution networks serve as the key interface between the main grid and end users. Their stability, power quality, and efficiency depend on accurate data management and analysis. Distribution networks generate large volumes of multi-source heterogeneous data that contain user consumption records, real-time meteorology, equipment status, and marketing information. These data streams often become incomplete during collection or transmission due to noise, sensor failures, equipment aging, or adverse weather. Missing data reduces the reliability of real-time monitoring and affects essential tasks such as load forecasting, fault diagnosis, health assessment, and operational decision making. Conventional approaches such as mean or regression imputation lack the capacity to maintain temporal dependencies. Generative models such as Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs) do not represent the complex statistical characteristics of grid data with sufficient accuracy. This study proposes a diffusion model based data recovery method for distribution networks. The method is designed to reconstruct missing data, preserve semantic and statistical integrity, and enhance data utility to support smart grid stability and efficiency.MethodsThis paper proposes a power grid data augmentation method based on diffusion models. The core of the method is that input Gaussian noise is mapped to the target distribution space of the missing data so that the recovered data follows its original distribution characteristics. To reduce semantic discrepancy between the reconstructed data and the actual data, the method uses time series sequence embeddings as conditional information. This conditional input guides and improves the diffusion generation process so that the imputation remains consistent with the surrounding temporal context.Results and DiscussionsExperimental results show that the proposed diffusion model based data augmentation method achieves higher accuracy in recovering missing power grid data than conventional approaches. The performance demonstrates that the method improves the completeness and reliability of datasets that support analytical tasks and operational decision making in smart grids.ConclusionsThis study proposes and validates a diffusion model based data augmentation method designed to address data missingness in power distribution networks. Traditional restoration methods and generative models have difficulty capturing the temporal dependencies and complex distribution characteristics of grid data. The method presented here uses temporal sequence information as conditional guidance, which enables accurate imputation of missing values and preserves the semantic integrity and statistical consistency of the original data. By improving the accuracy of distribution network data recovery, the method provides a reliable approach for strengthening data quality and supports the stability and efficiency of smart grid operations.

研究背景与目标：智能电网建设推动电力系统现代化发展，配电网络是主电网与终端用户之间的关键接口。其稳定性、电能质量与运行效率依赖于精准的数据管理与分析。配电网络会产生海量多源异构数据，涵盖用户用电记录、实时气象数据、设备运行状态以及营销业务信息。由于噪声干扰、传感器故障、设备老化或恶劣天气等因素，这些数据流在采集或传输过程中时常出现缺失。数据缺失会降低实时监测的可靠性，影响负荷预测、故障诊断、健康评估以及运行决策等核心任务的开展。传统的均值插补、回归插补等方法无法保留数据的时间依赖关系。生成式对抗网络（Generative Adversarial Networks，GANs）、变分自编码器（Variational AutoEncoders，VAEs）等生成模型，也难以精准表征电网数据的复杂统计特征。本研究提出一种面向配电网络的基于扩散模型（diffusion model）的数据恢复方法，该方法旨在重构缺失数据，保留语义与统计完整性，提升数据可用性，为智能电网的稳定运行与效率提升提供支撑。

方法：本文提出一种基于扩散模型的电网数据增强方法。该方法的核心在于将输入的高斯噪声映射至缺失数据的目标分布空间，使恢复后的数据契合原始分布特征。为降低重构数据与实际数据间的语义偏差，本方法采用时间序列嵌入作为条件信息，通过该条件输入引导并优化扩散生成过程，确保插补结果与周边时序上下文保持一致。

结果与讨论：实验结果表明，相较于传统方法，本文提出的基于扩散模型的数据增强方法在恢复缺失电网数据时具备更高的精度。该性能证明，本方法能够提升支撑智能电网分析任务与运行决策的数据集的完整性与可靠性。

结论：本研究提出并验证了一种面向配电网络数据缺失问题的基于扩散模型的数据增强方法。传统恢复方法与生成模型难以捕捉电网数据的时序依赖关系与复杂分布特征。本方法借助时序序列信息作为条件引导，能够精准插补缺失值，保留原始数据的语义完整性与统计一致性。通过提升配电网络数据恢复的精度，本方法为优化数据质量提供了可靠路径，同时为智能电网的稳定运行与效率提升提供了支撑。